Search Results - (( data estimation clustering algorithm ) OR ( data optimization method algorithm ))

Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

“…The K-Means algorithm is the commonest and fast technique in partitional cluster algorithms, although with unnormalized datasets it can achieve local optimal. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

“…Nature-inspired optimization-based clustering techniques are powerful, robust and more sophisticated than the conventional clustering methods due to their stochastic and heuristic characteristics. …”
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Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohamad Fadhil, Abas, Bayuaji, Luhur

“…DNN techniques is suitable in solving nonlinear and complex problem. The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

“…Hybridizing the Deep Neural Network (DNN) with the K-Means Clustering algorithm will increase the accuracy and reduce the data complexity of the Lorenz dataset. …”
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Optimization of ANFIS with GA and PSO estimating α ratio in driven piles by Moayedi, Hossein, Raftari, Mehdi, Sharifi, Abolhasan, Wan Jusoh, Wan Amizah, A. Rashid, Ahmad Safuan

“…The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. …”
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EZ-SEP: extended Z-SEP routing protocol with hierarchical clustering approach for wireless heterogeneous sensor network by Nurlan, Zhanserik, Zhukabayeva, Tamara, Othman, Mohamed

“…This article, introduces a new heterogeneous-aware routing protocol well known as Extended Z-SEP Routing Protocol with Hierarchical Clustering Approach for Wireless Heterogeneous Sensor Network or EZ-SEP, where the connection of nodes to a base station (BS) is done via a hybrid method, i.e., a certain amount of nodes communicate with the base station directly, while the remaining ones form a cluster to transfer data. …”
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Fuzzy C means imputation of missing values with ant colony optimization by Mausor, F.H., Jaafar, J., Mohdtaib, S.

“…This error should be handled correctly before data is processed into processing model. This paper proposes a improved method of imputation by employing a new version of Fuzzy c Means (FCM) which hybridized with Evolutionary Algorithm to handle missing values problem. …”
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Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei by Chen , Shilei

“…For the unsupervised learning method, the hierarchical cluster analysis can correctly cluster the samples in terms of their damage states. …”
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Time series modeling of water level at Sulaiman Station, Klang River, Malaysia by Galavi, Hadi

“…The estimation of parameters of the model is accomplished using the hybrid learning algorithm consisting of standard neural network backpropagation algorithm and least squares method. …”
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Indoor positioning using weighted magnetic field signal distance similarity measure and fuzzy based algorithms by Bundak, Caceja Elyca

“…Therefore, for the second objective, another algorithm named the fuzzy algorithm is designed which combines the clustering algorithm, matching algorithm, triangle area algorithm and average Euclidean algorithm used to estimate location. …”
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Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system by Bundak, Caceja Elyca, Abd Rahman, Mohd Amiruddin, Abdul Karim, Muhammad Khalis, Osman, Nurul Huda

“…Then, we create a rank cluster algorithm where we match the top 10 ranks RPs with the nearest Euclidean distance to the TP with the RPs cluster. …”
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Datasets Size: Effect on Clustering Results by Raheem, Ajiboye Adeleke, Ruzaini, Abdullah Arshah, Hongwu, Qin

“…In this paper, we proposed a research technique that implements descriptive algorithms on numeric datasets of varied sizes. We modeled each subset of our data using EM clustering algorithm; two different numbers of partitions (k) were estimated and used for each experiment. …”
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Semiparametric binary model for clustered survival data by Arlin, Rifina, Ibrahim, Noor Akma, Arasan, Jayanthi, Abu Bakar, Mohd Rizam

“…This paper considers a method to analyze semiparametric binary models for clustered survival data when the responses are correlated. …”
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Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering by Davari, Atefeh, Marhaban, Mohammad Hamiruce, Mohd Noor, Samsul Bahari, Karimadini, Mohammad, Karimoddini, Ali

“…The algorithm also improves the calculations of shape and width of membership functions by means of clustering in order to improve the accuracy. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari by Satari, Siti Zanariah

“…Here, we introduce a measure of similarity based on the circular distance and obtain a cluster tree using the single linkage clustering algorithm. …”
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Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid by Gaeid, Khalaf Salloum

“…The optimal levels of decomposition of the stator current error signal and mother wavelet function are selected with the help of the maximum entropy and description length data. …”
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Segmentation of MRI brain images using statistical approaches by Balafar, Mohammad Ali

“…Moreover, three improvements of EM for brain MRI segmentation are proposed, which incorporate neighbourhood information in a new manner in the clustering process. In addition, two algorithms for the post-processing of clustering results using user-interaction and the re-evaluation of boundary data in each cluster are presented. …”
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Expectation maximization clustering algorithm for user modeling in web usage mining system by Mustapha, Norwati, Jalali, Manijeh, Jalali, Mehrdad

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The multiple outliers detection for circular univariate data using different agglomerative clustering algorithms by Nur Syahirah, Zulkipli, Siti Zanariah, Satari, Wan Nur Syahidah, Wan Yusoff

“…Then, the results performance of the agglomerative clustering algorithms were compared and the best method for certain data conditions is chosen. …”
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Detection of multiple outliners in linear regression using nonparametric methods by Adnan, Robiah

“…REFERENCES Agullo, J. (2000). New Algorithms for Computing the Least Trimmed Squares Regression Estimator. …”
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